Lies; Damn Lies; and Forecasting…

NoEstimates has made a lot of traction over the last few years, with good reason, it is primarily about adopting Agile properly, delivering the valuable work in order of priority and in small chunks, and by doing so eliminating the need for a heavy duty estimation process. If we are only planning for the next delivery we can reliably forecast.

But sadly that is generally not good enough and some level of forecasting is often requested. So NoEstimates came up with a very useful and low cost method of forecasting. However, it has brought with it a whole host of misunderstandings, most of which are not from the book. The author must be as frustrated as anyone by the misinterpretation of his proposal. This has led to resistance from many (including me) to adopting this method for forecasting. I am all for delivering value quickly and small chunks or prioritized work, but slogans that are used to excuse bad behaviour are damaging and hard to resolve, especially when they seem so simple.

My biggest bugbear and one I have covered previously is that many have interpreted NoEstimates as an excuse to skip story refining entirely, this was not in the book but nevertheless you can see any number of articles on the internet professing how adopting NoEstimates has saved them from wasteful refining meetings, the misconception is that if you don’t need to estimate the story then the act of understanding the story is no longer required. When actually the author was suggesting that you don’t need to refine all work up front and could defer deeper understanding until it became relevant – the last responsible moment.

Story Writing and being Estimable

I encourage those writing stories to use the INVEST model for assessing the suitability of a story and in that: the ‘E’ is Estimable, but that doesn’t mean you must actually estimate the story, just that you ask yourself whether the story is clear enough and well understood enough to estimate if asked – are there open questions? is it clear what the acceptance criteria are and that these can be met? There may be a subtle distinction there, but NoEstimates does not offer an alternative to writing and refining good stories. It is just a method for simple forecasting and encouraging deferring effort until it is necessary.

How does NoEstimates work?

Caveat aside I will try to give a very high level summary of how NoEstimates forecasting works, and when and where it doesn’t work. I shall do so via the medium of potatoes.

Preparing Dinner

I have a pile of potatoes on the side and I am peeling them ready for a big family dinner. My wife asks me how much longer will it take me? By counting how many potatoes I have peeled in the last 5 minutes (10) and by counting the potatoes I still have left to do (30) I can quickly and simply calculate a forecast of 15 minutes.

That is NoEstimates forecasting in a nutshell, it really is that simple.

Assumptions

However, the mathematics requires a certain set of assumptions,

1. I did not apply any sorting criteria to the potatoes I selected- e.g. I wasn’t picking either small or large potatoes, we assume my selection was random or at least consistent with how I will behave in the future.

2. That the team doing the work doesn’t change, if my son were to take over to finish the job he may very well be faster or slower than me and my forecast would not be useful.

3. We also assume that I will not get faster

4. We assume that all potatoes in the backlog will be peeled, and no others will be added. If my wife asks me to peel more potatoes or to do the carrots too, the forecast will no longer apply and will need revising.
So there we have it, a very simple and surprisingly accurate method for forecasting future work. But do you see any flaws to the system?

Flaws in the system

Flaw 1. Comparing potatoes with potatoes

The first flaw is that I am getting potatoes ready for roasting so I want them to be broadly similar in size, so when I get to peel a potato I am also sometimes slicing it, some potatoes only need peeling others may be sliced once and others more than once. Some potatoes are bad and I throw them away.

If my wife comes along and sees my pile of potatoes and asks how much longer it will take? I can look at my pile of potatoes I have completed in the last 5 minutes (18) and I can count the potatoes I still have left to (30). The problem is I don’t know how many unpeeled potatoes were needed to produce those 18 peeled and sliced potatoes, I am not comparing like for like. To be able to give this estimate I would have needed to count how many unpeeled potatoes I had peeled, information I don’t have. Maybe I could take a guess and then use that guess to extrapolate a forecast, but that sounds like guesswork rather than forecasting.

Flaw 2. Forecasting an unknown

Let’s assume that I am producing 10 peeled potatoes in 5 minutes, and I am asked to give a forecast as to when I will be done, but so far I have been grabbing a handful of potatoes at a time, peeling them and then going back for more, one could say that my backlog of work is not definitive, We have a whole sack of potatoes but I won’t use them all for this one meal. I am simply adding work as I need it. My aim being to judge when I am satisfied I am done and start cooking. It is very difficult for me to judge when the sack will be empty or when I have prepared enough for lunch.

Flaw 3. Changing and evolving work

It is a big family dinner and uncle Freddie has just called to say he will be coming so we need to add more food, Aunt Florance eats like a bird so probably not worth doing a full portion for her. And the table isn’t really big enough for everyone, so maybe we should do an early meal for the kids first. The point here is that simple forecasting only works if you have a reasonably good assessment of what the work is still to be done, if your backlog of work is evolving, work being added or removed then the forecast will be unstable.

Flaw 4. Assuming consistency

When selecting work to do next I have a tendency to choose the work that will bring me the most value for the least effort. The highest ROI, so in this case I may choose the small potatoes first, less peeling and less chopping. But that means that if I count my competed work and use that to forecast my future work I will end up underestimating how much is left, the backlog has some really big awkward shaped potatoes that will take far longer to do. But my forecast is based on only doing small simple potatoes.

Doesn’t this apply to all forms of estimates and forecasts?

Flaws 2 and 3 apply to any form of forecasting, they are not unique to NoEstimates. Flaw 1 and Flaw 4 could potentially be mitigated with the use of T-shirt sizing or story points, but to do so requires a level of upfront effort. Effort that is not spent on peeling potatoes, so may well be considered waste – that is unless you see value in a more reliable forecast.
For me Flaw 1 is my main objection to NoEstimates (beyond the belief that refining is unnecessary) When stories are refined and better understood it is normal to split or discard stories, and often add stories as the subject becomes better understood. So any forecasting tool that uses a metric based on counting refined stories to predict a backlog of unrefined stories is risking over simplification of the problem. But because the maths is so simple it can lead to a confidence level that exceeds the quality of the data. These assumptions based on flawed data gets even worse when you use a tool like Monte Carlo forecasting which applies a further confidence level to the forecast. By giving a date combined with a confidence level adds such a degree of validity and assurance that it is easy to forget that a forecast based on duff data will result in a duff estimate – no matter how prettily we dress it up.

Summary

Forecasting is risky at the best of times, especially in Agile where it is our goal to have the work evolve and change in order to give the customer what they truly want. Forecasting needs to be understood by both parties and accepted that it is an evolving and changing metric. Anyone expecting a forecast to be a commitment or to be static is likely to be disappointed. Just take a look at the weather forecast, the week ahead changes day by day, the further away the forecast the more unreliable it becomes. Understanding the limits of the forecasting method is crucial, a simple tool like NoEstimates is fantastic IF the assumptions can be satisfied, if they cannot then the forecast will be unreliable.

It is probably also true that your forecast will improve if you spend more effort understanding the work. Time spent refining the stories will improve your knowledge. But no forecast can reliably predict work you do not yet know about. The question as always is “What problem are you trying to solve by forecasting?” That will guide you in determining whether the up front effort is worth it.
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Published by John Yorke

I am the Operations Manager for World Wide Technology's Virtual Office - supporting teams of software consultants collaborating to deliver amazing products and solutions.
I am also an Agile Coach, coaching teams in developing an Agile, Lean and Theory of Constraints mindset. I am a regular speaker at conferences on related topics and am the designer of a Kanban based board game - "Motor City" I have 25 years experience in software development, working on projects in both the UK and the USA
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I am the Operations Manager for World Wide Technology's Virtual Office - supporting teams of software consultants collaborating to deliver amazing products and solutions.
I am also an Agile Coach, coaching teams in developing an Agile, Lean and Theory of Constraints mindset. I am a regular speaker at conferences on related topics and am the designer of a Kanban based board game - "Motor City" I have 25 years experience in software development, working on projects in both the UK and the USA